用扩散模型从核磁谱图自动推断分子结构
DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation
- 基于扩散模型迭代优化分子图，保证全局一致性
- 在多个数据集上达到先进水平，生成准确率显著提升
- 适合化学、药物研发人员快速解析复杂谱图
核磁共振（NMR）光谱是分子结构解析的核心手段，但因谱图数据复杂和化学空间庞大，从谱图推断结构仍具挑战。本文提出DiffNMR，一种端到端的条件离散扩散模型框架，用于从NMR谱图生成全新分子结构。该方法通过基于扩散的生成过程迭代精炼分子图，避免自回归方法中的误差累积，并确保全局一致性。框架采用两阶段预训练策略：首先通过扩散自编码器（Diff-AE）对齐谱图与分子表征，再结合对比学习；推理时引入检索初始化与相似性过滤，并使用带径向基函数（RBF）编码的专用NMR编码器处理化学位移，保持连续性和化学相关性。实验表明，DiffNMR在多个基准数据集上表现优异，为自动化分子分析提供了高效稳健的解决方案。
原文摘要 · Abstract (English)
Nuclear Magnetic Resonance (NMR) spectroscopy is a central characterization method for molecular structure elucidation, yet interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral data and the vastness of the chemical space. In this work, we introduce DiffNMR, a novel end-to-end framework that leverages a conditional discrete diffusion model for de novo molecular structure elucidation from NMR spectra. DiffNMR refines molecular graphs iteratively through a diffusion-based generative process, ensuring global consistency and mitigating error accumulation inherent in autoregressive methods. The framework integrates a two-stage pretraining strategy that aligns spectral and molecular representations via diffusion autoencoder (Diff-AE) and contrastive learning, the incorporation of retrieval initialization and similarity filtering during inference, and a specialized NMR encoder with radial basis function (RBF) encoding for chemical shifts, preserving continuity and chemical correlation. Experimental results demonstrate that DiffNMR achieves competitive performance for NMR-based structure elucidation, offering an efficient and robust solution for automated molecular analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。